Internet of Predictable Things (IoPT) Framework to Increase Cyber-Physical System Resiliency

• URL: http://arxiv.org/abs/2101.07816v1
• Date: Tue, 19 Jan 2021 19:01:56 GMT
• Title: Internet of Predictable Things (IoPT) Framework to Increase Cyber-Physical System Resiliency
• Authors: Umit Cali, Murat Kuzlu, Vinayak Sharma, Manisa Pipattanasomporn, Ferhat Ozgur Catak
• Abstract summary: This paper proposes the concept of the Internet of Predictable Things (IoPT) It incorporates advanced data analytics and machine learning methods to increase the resiliency of cyber-physical systems against cybersecurity risks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: During the last two decades, distributed energy systems, especially renewable energy sources (RES), have become more economically viable with increasing market share and penetration levels on power systems. In addition to decarbonization and decentralization of energy systems, digitalization has also become very important. The use of artificial intelligence (AI), advanced optimization algorithms, Industrial Internet of Things (IIoT), and other digitalization frameworks makes modern power system assets more intelligent, while vulnerable to cybersecurity risks. This paper proposes the concept of the Internet of Predictable Things (IoPT) that incorporates advanced data analytics and machine learning methods to increase the resiliency of cyber-physical systems against cybersecurity risks. The proposed concept is demonstrated using a cyber-physical system testbed under a variety of cyber attack scenarios as a proof of concept (PoC).

Related papers

• Digital Twin for Evaluating Detective Countermeasures in Smart Grid Cybersecurity [0.0]
  This study delves into the potential of digital twins, replicating a smart grid's cyber-physical laboratory environment. We introduce a flexible, comprehensive digital twin model equipped for hardware-in-the-loop evaluations.
  arXiv  Detail & Related papers  (2024-12-05T08:41:08Z)
• Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
  Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. GenAI can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• Leakage-Resilient and Carbon-Neutral Aggregation Featuring the Federated AI-enabled Critical Infrastructure [42.688679691088204]
  We propose a leakage-resilient, communication-efficient, and carbon-neutral approach for ACI networks. We show that CDPA can reduce communication cost by half while preserving model utility. We highlight existing benchmarks that generate 2.6x to over 100x more carbon emissions than CDPA.
  arXiv  Detail & Related papers  (2024-05-24T06:35:09Z)
• GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction [53.2306792009435]
  We propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints. Our findings reveal that an adversary armed solely with the stability model's output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99.
  arXiv  Detail & Related papers  (2024-05-20T14:43:46Z)
• Green Edge AI: A Contemporary Survey [46.11332733210337]
  The transformative power of AI is derived from the utilization of deep neural networks (DNNs) Deep learning (DL) is increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs) Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL.
  arXiv  Detail & Related papers  (2023-12-01T04:04:37Z)
• Securing the Digital World: Protecting smart infrastructures and digital industries with Artificial Intelligence (AI)-enabled malware and intrusion detection [0.0]
  cybercrime has emerged as a global threat to governments, businesses, and civil societies. This paper investigates AI-based cyber threat detection to protect our modern digital ecosystems.
  arXiv  Detail & Related papers  (2023-10-15T09:35:56Z)
• ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents [0.0]
  ANALYSE is a machine-learning-based software suite to let learning agents autonomously find attacks in cyber-physical energy systems. It is designed to find yet unknown attack types and to reproduce many known attack strategies in cyber-physical energy systems from the scientific literature.
  arXiv  Detail & Related papers  (2023-04-21T11:36:18Z)
• Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets [74.51865676466056]
  We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning. Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
  arXiv  Detail & Related papers  (2022-11-14T01:29:09Z)
• Web-Based Platform for Evaluation of Resilient and Transactive Smart-Grids [0.0]
  Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. We present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks.
  arXiv  Detail & Related papers  (2022-06-11T15:34:33Z)
• Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence [45.076180487387575]
  Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
  arXiv  Detail & Related papers  (2021-05-04T23:42:06Z)
• A Taxonomy of Cyber Defence Strategies Against False Data Attacks in Smart Grid [3.88835600711547]
  Modern electric power grid, known as the Smart Grid, has fast transformed the isolated and centrally controlled power system to a fast and massively connected cyber-physical system. The synergy of a vast number of cyber-physical entities has allowed the Smart Grid to be much more effective and sustainable in meeting the growing global energy challenges. However, it has also brought with it a large number of vulnerabilities resulting in breaches of data integrity, confidentiality and availability.
  arXiv  Detail & Related papers  (2021-03-30T05:36:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.